What the Data for This Category Looks Like
Ledger data for urban investment platforms mostly comes from internal project management systems, financing ledger systems and financial statement modules. Update cycles follow monthly or quarterly schedules; some special ledgers update weekly. Most single documents take the form of structured tables, containing fields such as ledger ID, project name, financing amount, due repayment date, guarantee entity and affiliated region. The unit for all amounts is ten thousand yuan, and the date format follows YYYY-MM-DD. Some documents also include unstructured paragraphs with project progress descriptions.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
The structured nature of urban investment platform ledgers requires retrieval to support precise matching by specified fields, to prevent irrelevant results from being included. The monthly and quarterly update frequency requires knowledge base synchronization tasks to align with the ledger update cycle, to avoid recalling outdated data. Single documents contain multi-dimensional fields, so recall logic must be adjusted to prioritize core fields such as ledger ID and project name, while also supporting the included unstructured project description text. Additionally, ledger data uses unified amount and date units. These units must be retained during retrieval to improve matching accuracy.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Meets the precision matching requirements for urban investment ledger fields, and prevents low-precision irrelevant ledgers from appearing in results |
limit | Top 12 entries | Covers the basic data volume required for a single ledger summary, while controlling the context length for report generation |
searchMode | vector+keyword | Combines the structured fields and unstructured descriptions of urban investment ledgers, balancing precise matching and semantic recall |
usingReRank | Enabled | Performs secondary sorting of recall results by relevance, to optimize the priority of core data for ledger report generation |
datasetSearchUsingExtensionQuery | Enabled | Generates extended queries based on original questions, to cover synonymous field terms in ledgers such as "due date" and "repayment date" |
collectionIds | Exclusive collection ID for urban investment ledgers | Limits the retrieval scope to only the urban investment platform's ledger dataset, avoiding interference from cross-category data |
Three Common Configuration Mistakes
- Phenomenon: Clicking the document reading link returned by the API triggers an error, prompting that only .txt format is supported. Cause: Uploaded ledger documents are unprocessed Excel or PDF files, and have not undergone format conversion via FastGPT's document preprocessing module. This results in links that only support plain text access.
- Phenomenon: The remote service generates abnormal logs after deployment, prompting that a TRACE request was triggered. Cause: TRACE request support for the web service in the FastGPT deployment environment was not disabled. This interferes with the retrieval link and poses a security risk.
- Phenomenon: The original text of uploaded ledger documents cannot be found in the knowledge base. Cause: FastGPT's document storage path was not configured correctly, or the dataset access permissions for the retrieval role were not limited. This results in failure to match the corresponding documents.
How to Confirm the Configuration Is Correct
- Run a single retrieval test, and verify that all returned document results belong to the exclusive urban investment ledger dataset, with no cross-category data included.
- Upload a test ledger document, trigger retrieval, and check that the returned results include the specified field content of this document. Confirm that the matching degree meets the preset
similarityrange requirements. - Call the API to generate a ledger report, and verify that the returned context length matches the number of result entries set by the
limitparameter, with no redundant data. - View the knowledge base synchronization logs, and confirm that synchronization tasks are completed according to the preset update cycle, with no timeout or failure records.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.